Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements

Beck, Hauke; Kühn, Martin

doi:10.3390/rs9060561

Cited by 60 publications

(55 citation statements)

References 36 publications

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“…The right figure shows the data distribution after applying the self‐defined filtering criteria. There seems to be no indication that data within the high data density region but below the CNR threshold is invalid . Based on this assumption, the applied filtering technique can be considered conservative as it cuts off CNR values below ‐25 dB, which are within the LOS wind velocity range of approximately ± 15 m/s.…”

Section: Measurement Campaignmentioning

confidence: 99%

“…There seems to be no indication that data within the high data density region but below the CNR threshold is invalid. 27 Based on this assumption, the applied filtering technique can be considered conservative as it cuts off CNR values below -25 dB, which are within the LOS wind velocity range of approximately ± 15 m/s. As a result of the application of the self-defined filters, the overall data availability increased by approximately 15% in comparison to the fixed -20-dB threshold filter while unrealistic values were filtered out.…”

Section: Data Processingmentioning

confidence: 99%

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LiDAR‐based characterization of mid‐altitude wind conditions for airborne wind energy systems

et al. 2019

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Summary Engineers and researchers working on the development of airborne wind energy systems (AWES) still rely on oversimplified wind speed approximations and coarsely sampled reanalysis data because of a lack of high‐resolution wind data at altitudes above 200 m. Ten‐minute average wind speed LiDAR measurements up to an altitude of 1100 m and data from nearby weather stations were investigated with regard to wind energy generation and impact on LiDAR measurements. Data were gathered by a long‐range pulsed Doppler LiDAR device installed on flat terrain. Because of the low overall carrier‐to‐noise ratio, a custom‐filtering technique was applied. Our analyses show that diurnal variation and atmospheric stability significantly affect wind conditions aloft which cause a wide range of wind speeds and a multimodal probability distribution that cannot be represented by a simple Weibull distribution fit. A better representation of the actual wind conditions can be achieved by fitting Weibull distributions separately to stable and unstable conditions. Splitting and clustering the data by simulated surface heat flux reveals substate stratification responsible for the multimodality. We classify different wind conditions based on these substates, which result in different wind energy potential. We assess optimal traction power and optimal operating altitudes statistically as well as for specific days based on a simplified AWES model. Using measured wind speed standard deviation, we estimate average turbulence intensity and show its variation with altitude and time. Selected short‐term data sets illustrate temporal changes in wind conditions and atmospheric stratification with a high temporal and vertical resolution.

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Section: Measurement Campaignmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

LiDAR‐based characterization of mid‐altitude wind conditions for airborne wind energy systems

et al. 2019

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“…Wake measurements were mainly performed using the remote-sensing technique Doppler lidar (e.g. Aitken et al, 2014;Trabucchi et al, 2017;Bodini et al, 2017;Fuertes et al, 2018;Beck and Kühn, 2019), power analysis on the basis of SCADA data (e.g. Barthelmie and Jensen, 2010) or Doppler radar (e.g.…”

Section: Introductionmentioning

confidence: 99%

Cluster wakes impact on a far-distant offshore wind farm's power

et al. 2020

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Abstract. Our aim with this paper was the analysis of the influence of offshore cluster wakes on the power of a far-distant wind farm. We measured cluster wakes with long-range Doppler light detection and ranging (lidar) and satellite synthetic aperture radar (SAR) in different atmospheric stabilities and analysed their impact on the 400 MW offshore wind farm Global Tech I in the German North Sea using supervisory control and data acquisition (SCADA) power data. Our results showed clear wind speed deficits that can be related to the wakes of wind farm clusters up to 55 km upstream in stable and weakly unstable stratified boundary layers resulting in a clear reduction in power production. We discussed the influence of cluster wakes on the power production of a far-distant wind farm, cluster wake characteristics and methods for cluster wake monitoring. In conclusion, we proved the existence of wake shadowing effects with resulting power losses up to 55 km downstream and encouraged further investigations on far-reaching wake shadowing effects for optimized areal planning and reduced uncertainties in offshore wind power resource assessment.

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“…For optimizing data handling, all measurements were grouped into 1‐hour intervals. Data quality was ensured by employing a data density filter . Outliers and invalid measurement points were identified and removed using the normalized data density within the carrier‐to‐noise ratio and the radial speed reference frame.…”

Section: Methodsmentioning

confidence: 99%

Field investigation on the influence of yaw misalignment on the propagation of wind turbine wakes

et al. 2018

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A comprehensive understanding of the wake development of wind turbines is essential for improving the power yield of wind farms and for reducing the structural loading of the turbines.Reducing the overall negative impact of wake flows on individual turbines in a farm is one goal of wind farm control. We aim to demonstrate the applicability of yaw control for deflecting wind turbine wakes in a full-scale field experiment. For this purpose, we conducted a measurement campaign at a multimegawatt onshore wind turbine including inflow and wake flow measurements using ground-and nacelle-based long-range light detection and ranging devices. Yaw misalignments of the turbine with respect to the inflow direction of up to 20 • were investigated. We were able to show that under neutral atmospheric conditions, these turbine misalignments cause lateral deflections of its wake. Larger yaw misalignments resulted in greater wake deflection.Because of the inherent struggle in capturing complex and highly dynamic ambient conditions in the field using a limited number of sensors, we particularly focused on providing a comprehensive and comprehensible description of the measurement setup, including the identification of potential uncertainties. KEYWORDSatmospheric boundary layer, atmospheric inflow, lidar, wake deflection, wind farm control INTRODUCTIONWind turbines in a wind farm are typically subjected to mutual aerodynamic interactions due to their wakes. Depending on the farm layout and inflow direction, this can lead to unfavourable structural loading and a substantial reduction in the power yield over extended periods of time . 1-3 Reducing the overall negative impact of wake flows on individual turbines in a farm is one goal of wind farm control. However, a successful implementation of control mechanisms requires a broad understanding of the flow development for various ambient conditions and the interaction between turbines and the flow.One specific approach that has proven its potential in simulations and wind tunnel experiments is wake deflection through turbine operation under yaw misalignment. In this case, an offset between the inflow direction and the orientation of a turbine is deliberately introduced to alter its wake trajectory. Hereinafter, we refer to this method as yaw control for the sake of simplicity. It aims to generate more favourable inflow conditions for downstream turbines by reducing the wake effects. This can, for example, be used to maximize the power output of a wind farm, to mitigate power fluctuation or to reduce turbine loads. With respect to power maximization, it should be considered that misaligned wind turbines generate less power. Therefore, it must be ensured that the power increase generated by the downstream turbines is sufficient for improving the overall power yield of the wind farm.The concept of wake deflection has already been successfully applied in wind tunnel experiments by Clayton and Filby in 1982. 4 Further investigations of the power yield and characteristics of the downstream d...

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Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements

Cited by 60 publications

References 36 publications

LiDAR‐based characterization of mid‐altitude wind conditions for airborne wind energy systems

LiDAR‐based characterization of mid‐altitude wind conditions for airborne wind energy systems

Cluster wakes impact on a far-distant offshore wind farm's power

Field investigation on the influence of yaw misalignment on the propagation of wind turbine wakes

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